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Dinesh Kumar, P.
- Coral Mortality in the Gulf of Mannar, Southeastern India, Due to Bleaching Caused by Elevated Sea Temperature in 2016
Abstract Views :263 |
PDF Views:105
Authors
J. K. Patterson Edward
1,
G. Mathews
1,
K. Diraviya Raj
1,
R. L. Laju
1,
M. Selva Bharath
1,
A. Arasamuthu
1,
P. Dinesh Kumar
1,
Deepak S. Bilgi
2,
H. Malleshappa
3
Affiliations
1 Suganthi Devadason Marine Research Institute, 44-Beach Road, Tuticorin 628 001, IN
2 Gulf of Mannar Marine National Park, Ramanathapuram 623 503, IN
3 Department of Environment, Govt of Tamil Nadu, Chennai 600 015, IN
1 Suganthi Devadason Marine Research Institute, 44-Beach Road, Tuticorin 628 001, IN
2 Gulf of Mannar Marine National Park, Ramanathapuram 623 503, IN
3 Department of Environment, Govt of Tamil Nadu, Chennai 600 015, IN
Source
Current Science, Vol 114, No 09 (2018), Pagination: 1967-1972Abstract
Intensive underwater surveys have been conducted to assess the extent of coral bleaching and subsequent mortality in the Gulf of Mannar between March and October 2016. The extent of bleaching was 23.92% ± 10.55% during the period between March and June 2016, and the live coral cover was drastically reduced to 22.69 ± 9.07% during October 2016 with a mortality of 16.17 ± 8.46%. Fast-growing coral forms, including the genera Acropora, Montipora and Pocillopora were most affected, not only by bleaching but also by severe mortality. Boulders, including the genera Porites, Favia and Favites were found to be resistant to bleaching. During the bleaching period, water temperature was between 31.2°C and 32.6°C. The current bleaching is in alignment with the third global coral bleaching event which occurred between 2014 and 2017. Management interventions, including protection and rehabilitation using the native resistant coral species will not only help in the recovery process, but also increase the live coral cover.Keywords
Bleaching, Climate Change, Coral Reefs, Mortality, Sea Surface Temperature.References
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- Edward, J. K. P., Mathews, G., Patterson, J., Wilhelmsson, D., Tamelander, J. and Linden, O., Coral reefs of the Gulf of Mannar, southeastern India – distribution, diversity and status. Suganthi Devadason Marine Research Institute Special Publication no. 12, 2007.
- Edward, J. K. P., Mathews, G., Raj, K. D. and Tamelander, J., Coral reefs of the Gulf of Mannar, southeastern India – observations on the effect of elevated SST during 2005−2008. In Proceedings of the 11th International Coral Reef Symposium, Fort Lauderdale, Florida, USA, 2008.
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- Raj, K. D., Mathews, G. and Edward, J. K. P., Reproductive success of restored coral colonies in Vaan Island, Gulf of Mannar, Southeastern India. Indian J. Geomarine Sci., 2015, 44(4), 589–598.
- Predicting the Area and Production of Sugarcane in Tamil Nadu, India Using Neural Networks
Abstract Views :103 |
PDF Views:60
Authors
Affiliations
1 Department of Agriculture, Karunya University, Coimbatore 741 114, IN
1 Department of Agriculture, Karunya University, Coimbatore 741 114, IN
Source
Current Science, Vol 124, No 4 (2023), Pagination: 500-504Abstract
Sugarcane is a major cash crop in India, grown in almost 5 million hectares with a production of 339 million tonnes. Tamil Nadu contributes significantly to the production of sugarcane. Data from the past year show a huge fluctuation in the area and production of sugarcane in the state. Predicting the area and production employing traditional modelling techniques fails because the assumptions are never attained in the field. To overcome this, soft computing techniques like artificial neural networks (ANNs) are used. In this study, a multilayer perceptron neural network (MLP-NN) with back-propagation was used to predict the area and production of sugarcane in Tamil Nadu. The MLP-NN (2,2) model predicts the area with minimum mean absolute error (MAE; 18.139) and root mean squared error (RMSE; 23.058) values and with high accuracy (99%). For production, the MLP-NN (2,1) model estimates minimum MAE (24.875) and RMSE (31.199) values with high accuracy (99%). So, MLP-NN (2,2) and MLP-NN (2,1) are the best ANN models to predict the area and production of sugarcane in Tamil Nadu respectively. Additionally, ANN models perform better in predicting nonlinear stochastic data.Keywords
Back Propagation, Multilayer Perceptron, Neural Network, Nonlinear Stochastic Data, Sugarcane Area and Production.References
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